MEE 1107 Pattern Recognition

MEE 1107
Pattern
Recogni
tion

Module

-

I

Basics of pattern recognition

: Overview of pattern recognition,
Pattern Recognition Systems,
Classification and Description, Pattterns and Feature Extraction, Training and Learning methods,
Pattern

Recognition approaches.

Module
-

II

Bayesian decision theory

: Classifiers, Discriminant functions, Decision surfaces, Normal density
and discriminant functions, Discrete features
,
Parameter estimation methods
,

Maximum
-
Likelihood
estimation, Gaussian mixt
ure models, Expectation
-
maximization method
,

Bayesian estimation

Module
-

I
II

Hidden Markov models for sequential pattern classification

:

Discrete hidden Markov models
,

Contin
u
ous density hidden Markov models
,
Dimension reduction methods
,

Fisher
discriminant
analysis
,

Principal component analysis

Module

-

I
V

Non
-
parametric techniques for density

estimation

:

Parzen
-
window method
,
K
-
Nearest Neighbour
method

Module
-

V

Linear discriminant function based

classifiers

:

Perceptron
,

Support vector mach
ines
,
Multicategory Generalization

Module
-

V
I

Non
-
metric methods for pattern classification

:

Non
-
numeric data or nominal data
,

Decision trees

Module
-

VII

Unsupervised learning and

clustering

:

Criterion functions for clustering
,

Algorithms for
clustering: K
-
means, Hierarchical and other methods
,

Cluster validation

Text Books:

1.

R.O.Duda, P.E.Hart and D.G.Stork, Pattern Classification, John Wiley, 2001

Reference Books:



2. S.Theodoridis and K.Koutroumbas
, Pattern Recognition, 4th Ed., Academic Press, 2009


3. C.M.Bishop, Pattern Recognition and Machine Learning, Springer, 2006